Artificial Intelligence: Reinforcement Learning in Python from Udemy

Ever wondered how AI technologies like OpenAI ChatGPT and GPT-4 really work? In this course, you will learn the foundations of these groundbreaking applications.

When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.

These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level.

Reinforcement learning has recently become popular for doing all of that and more.

Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.

In 2016 we saw Google’s AlphaGo beat the world Champion in Go.

We saw AIs playing video games like Doom and Super Mario.

Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.

If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.

Learning about supervised and unsupervised machine learning is no small feat. To date I have over TWENTY FIVE (25. ) courses just on those topics alone.

And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is very from both supervised and unsupervised learning.

It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true artificial general intelligence. What’s covered in this course?

The multi-armed bandit problem and the explore-exploit dilemma
Ways to calculate means and moving averages and their relationship to stochastic gradient descent
Markov Decision Processes (MDPs)
Dynamic Programming
Monte Carlo
Temporal Difference (TD) Learning (Q-Learning and SARSA)
Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)
How to use OpenAI Gym, with zero code changes
Project: Apply Q-Learning to build a stock trading bot

If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.

See you in class.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

Calculus
Probability
Object-oriented programming
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations
Linear regression
Gradient descent

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

What's inside

Learning objectives

Apply gradient-based supervised machine learning methods to reinforcement learning
Understand reinforcement learning on a technical level
Understand the relationship between reinforcement learning and psychology

Implement 17 different reinforcement learning algorithms
Understand important foundations for openai chatgpt, gpt-4

Apply gradient-based supervised machine learning methods to reinforcement learning
Understand reinforcement learning on a technical level
Understand the relationship between reinforcement learning and psychology
Implement 17 different reinforcement learning algorithms
Understand important foundations for openai chatgpt, gpt-4

Syllabus

Welcome

Introduction

Course Outline and Big Picture

Where to get the Code

How to Succeed in this Course

Warmup

Return of the Multi-Armed Bandit

Section Introduction: The Explore-Exploit Dilemma

Applications of the Explore-Exploit Dilemma

Epsilon-Greedy Theory

Calculating a Sample Mean (pt 1)

Epsilon-Greedy Beginner's Exercise Prompt

Designing Your Bandit Program

Epsilon-Greedy in Code

Comparing Different Epsilons

Optimistic Initial Values Theory

Optimistic Initial Values Beginner's Exercise Prompt

Optimistic Initial Values Code

UCB1 Theory

UCB1 Beginner's Exercise Prompt

UCB1 Code

Bayesian Bandits / Thompson Sampling Theory (pt 1)

Bayesian Bandits / Thompson Sampling Theory (pt 2)

Thompson Sampling Beginner's Exercise Prompt

Thompson Sampling Code

Thompson Sampling With Gaussian Reward Theory

Thompson Sampling With Gaussian Reward Code

Exercise on Gaussian Rewards

Why don't we just use a library?

Nonstationary Bandits

Bandit Summary, Real Data, and Online Learning

(Optional) Alternative Bandit Designs

Suggestion Box

High Level Overview of Reinforcement Learning

What is Reinforcement Learning?

From Bandits to Full Reinforcement Learning

Markov Decision Proccesses

MDP Section Introduction

Gridworld

Choosing Rewards

The Markov Property

Markov Decision Processes (MDPs)

Future Rewards

Value Functions

The Bellman Equation (pt 1)

The Bellman Equation (pt 2)

The Bellman Equation (pt 3)

Bellman Examples

Optimal Policy and Optimal Value Function (pt 1)

Optimal Policy and Optimal Value Function (pt 2)

MDP Summary

Dynamic Programming

Dynamic Programming Section Introduction

Iterative Policy Evaluation

Designing Your RL Program

Gridworld in Code

Iterative Policy Evaluation in Code

Windy Gridworld in Code

Iterative Policy Evaluation for Windy Gridworld in Code

Policy Improvement

Policy Iteration

Policy Iteration in Code

Policy Iteration in Windy Gridworld

Value Iteration

Value Iteration in Code

Dynamic Programming Summary

Monte Carlo

Monte Carlo Intro

Monte Carlo Policy Evaluation

Monte Carlo Policy Evaluation in Code

Monte Carlo Control

Monte Carlo Control in Code

Monte Carlo Control without Exploring Starts

Monte Carlo Control without Exploring Starts in Code

Monte Carlo Summary

Temporal Difference Learning

Temporal Difference Introduction

TD(0) Prediction

TD(0) Prediction in Code

SARSA

SARSA in Code

Q Learning

Q Learning in Code

TD Learning Section Summary

Approximation Methods

Approximation Methods Section Introduction

Linear Models for Reinforcement Learning

Feature Engineering

Approximation Methods for Prediction

Approximation Methods for Prediction Code

Approximation Methods for Control

Approximation Methods for Control Code

CartPole

CartPole Code

Approximation Methods Exercise

Approximation Methods Section Summary

Interlude: Common Beginner Questions

This Course vs. RL Book: What's the Difference?

Stock Trading Project with Reinforcement Learning

Beginners, halt! Stop here if you skipped ahead

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Applies gradient-based supervised machine learning methods to reinforcement learning, useful for learners with a background in supervised machine learning

Develops a technical understanding of reinforcement learning, beneficial for those seeking a deep dive

Explores links between reinforcement learning and psychology, illuminating the practical applications of reinforcement learning

Provides hands-on implementation of 17 reinforcement learning algorithms, building practical skills

Lays out foundational concepts relevant to OpenAI ChatGPT and GPT-4, catering to learners interested in cutting-edge AI

Learners should have a strong foundation in calculus, probability, object-oriented programming, Numpy coding, and linear regression, making it unsuitable for absolute beginners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Artificial Intelligence: Reinforcement Learning in Python with these activities:

Review Sutton & Barto's Reinforcement Learning

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Provide a strong theoretical foundation for reinforcement learning concepts and improve your understanding of the technical aspects of the field.

View Reinforcement Learning, second edition: An... on Amazon

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Read chapters 1-4 to gain an overview of RL and Markov Decision Processes (MDPs).
Work through the exercises at the end of each chapter to test your comprehension.
Summarize the key concepts in each chapter in your own words to enhance retention.
Discuss the concepts with peers or mentors to deepen your understanding.

Mentor Junior Reinforcement Learning Learners

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Solidify your understanding by explaining concepts to others and helping them overcome challenges.

Browse courses on Reinforcement Learning

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Identify opportunities to mentor junior learners, such as through online forums or tutoring programs.
Provide guidance and support to learners who are struggling with RL concepts.
Explain complex ideas in a clear and concise manner.
Share your experiences and insights to help learners avoid common pitfalls.

Participate in Peer Study Groups

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Enhance your learning through discussions, shared insights, and collaborative problem-solving with peers.

Browse courses on Reinforcement Learning

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Form or join a study group with classmates.
Set regular meeting times and establish a study plan.
Discuss course concepts, share different perspectives, and work on exercises together.
Seek support and clarify doubts within the group.

Four other activities

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Show all seven activities

Follow Hands-on RL Tutorials on OpenAI Gym

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Develop practical skills in implementing RL algorithms by following guided tutorials using OpenAI Gym.

Browse courses on Reinforcement Learning

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Choose a tutorial that aligns with your learning goals.
Follow the tutorial step-by-step, experimenting with different parameters.
Modify the code to explore variations and gain a deeper understanding of the algorithms.
Troubleshoot any issues you encounter and seek help from online forums or mentors if needed.

Solve Reinforcement Learning Coding Exercises

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Strengthen your coding abilities in RL by solving challenging coding exercises.

Browse courses on Reinforcement Learning

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Find coding exercises from online platforms or textbooks.
Attempt to solve the exercises on your own, referring to course materials if needed.
Compare your solutions with provided answers or discuss them with peers.
Identify areas for improvement and focus on strengthening those skills.

Build a Reinforcement Learning Project

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Apply your knowledge to solve a real-world problem using RL, deepening your understanding and showcasing your skills.

Browse courses on Reinforcement Learning

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Identify a suitable problem for your project, ensuring it aligns with course concepts.
Design and implement your RL solution, documenting your process and decisions.
Evaluate the performance of your project and analyze the results.
Present your project to your peers or instructors for feedback and insights.

Participate in Reinforcement Learning Competitions

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Test your skills and push your understanding to the limit by participating in RL competitions.

Browse courses on Reinforcement Learning

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Identify and register for relevant RL competitions.
Develop and optimize your RL strategies.
Submit your solutions and track your progress.
Analyze the results and learn from both successes and failures.

Career center

Learners who complete Artificial Intelligence: Reinforcement Learning in Python will develop knowledge and skills that may be useful to these careers:

Artificial Intelligence Engineer

Artificial Intelligence Engineers design and develop artificial intelligence systems. This course provides a comprehensive introduction to reinforcement learning, a key subfield of artificial intelligence. By mastering the principles and techniques covered in this course, Artificial Intelligence Engineers gain a strong foundation for developing intelligent systems that can learn from their experiences and adapt to changing environments.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. This course delves into the fundamentals of reinforcement learning, providing a deep understanding of how to build systems that can learn from their experiences. Reinforcement learning is particularly relevant in areas such as robotics, game development, and autonomous vehicles, making this course highly valuable for aspiring Machine Learning Engineers seeking to specialize in these fields.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists are experts in the field of data analysis and machine learning, using their skills to extract insights from large datasets. This course provides a solid foundation in reinforcement learning, a key subfield of machine learning, making it a valuable resource for aspiring Data Scientists. By understanding the principles of reinforcement learning, learners can develop models for decision-making and optimization tasks, a crucial skill for Data Scientists working with complex data.

See salaries and explore the career path for Data Scientist

Academic Researcher

Academic Researchers conduct original research in various fields, including computer science and artificial intelligence. This course offers a deep dive into reinforcement learning, a cutting-edge subfield of machine learning. By gaining advanced knowledge in reinforcement learning, Academic Researchers can make significant contributions to the field and push the boundaries of artificial intelligence research.

See salaries and explore the career path for Academic Researcher

Research Scientist

Research Scientists conduct scientific research in various fields, including machine learning and artificial intelligence. This course offers a comprehensive introduction to reinforcement learning, a cutting-edge subfield of machine learning. By mastering the principles and techniques covered in this course, Research Scientists gain a strong foundation for conducting research in areas such as robotics, natural language processing, and decision-making systems.

See salaries and explore the career path for Research Scientist

Robotics Engineer

Robotics Engineers design, build, and maintain robots. This course provides a foundation in reinforcement learning, a technique used to develop intelligent systems that can learn from their experiences. By understanding reinforcement learning, Robotics Engineers can build more autonomous and adaptive robots, capable of performing complex tasks in various environments.

See salaries and explore the career path for Robotics Engineer

Game Designer

Game Designers create and design video games. This course introduces reinforcement learning, a technique used to develop intelligent systems that can learn from their interactions with the environment. By understanding reinforcement learning, Game Designers can create more engaging and challenging games, particularly in areas such as artificial intelligence opponents and procedural content generation.

See salaries and explore the career path for Game Designer

Software Engineer

Software Engineers design, develop, and maintain software applications. This course provides a practical foundation in reinforcement learning, a technique used to develop intelligent systems that can learn from their interactions with the environment. By gaining proficiency in reinforcement learning, Software Engineers can build more sophisticated and autonomous software systems, particularly in areas such as robotics, gaming, and optimization.

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Quantitative Analyst

Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data and make investment decisions. This course introduces reinforcement learning, a powerful technique for optimizing decision-making in complex environments. By understanding reinforcement learning, Quants can develop more sophisticated trading strategies and models, potentially leading to improved investment performance.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to improve the efficiency and effectiveness of organizations. This course introduces reinforcement learning, a technique used to develop intelligent systems that can learn from their interactions with the environment. By understanding reinforcement learning, Operations Research Analysts can develop more sophisticated models for resource allocation, scheduling, and supply chain management, potentially leading to significant operational improvements.

See salaries and explore the career path for Operations Research Analyst

Data Analyst

Data Analysts collect, clean, and analyze data to extract meaningful insights. This course introduces reinforcement learning, a technique used to train models for decision-making and optimization. By gaining proficiency in reinforcement learning, Data Analysts can develop more sophisticated data analysis pipelines and models, enabling them to derive deeper insights from complex datasets.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts analyze financial data and make recommendations on investments and financial decisions. This course provides an overview of reinforcement learning, a technique used to develop intelligent systems that can learn from their experiences. By understanding reinforcement learning, Financial Analysts can develop more sophisticated models for risk assessment, portfolio optimization, and trading strategies.

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Product Manager

Product Managers are responsible for managing the development and launch of new products. This course provides an overview of reinforcement learning, a technique used to create intelligent systems that can adapt to changing environments. By understanding reinforcement learning, Product Managers can develop more innovative and user-friendly products, particularly in areas such as personalized recommendations and dynamic pricing.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts assess business needs and develop solutions to improve efficiency and productivity. This course provides a foundation in reinforcement learning, a technique used to optimize decision-making in complex environments. By understanding reinforcement learning, Business Analysts can develop more effective solutions for a wide range of business problems, particularly in areas such as supply chain management and customer relationship management.

See salaries and explore the career path for Business Analyst

Consultant

Consultants provide expert advice and guidance to organizations on various business matters. This course introduces reinforcement learning, a technique used to develop intelligent systems that can learn from their interactions with the environment. By understanding reinforcement learning, Consultants can provide more innovative and data-driven solutions to their clients, particularly in areas such as process optimization and strategic planning.

See salaries and explore the career path for Consultant